PEST DETECTION USING IMAGE PROCESSING e The principal idea which empowered us to work on the project PEST DETECTION USING IMAGE PROCESSING is to ensure improved and better farming techniques for farmers. Our Solution: The techniques of image analysis are extensively applied to agricultural science, and it provides maximum protection to crops and also much less use of pesticides which can ultimately lead to better crop management and production. The following softwares are required for the project: OpenCV with C++/Python : It is a library which is designed for computational efficiency with a strong focus on real time applications. Pest Detection System Following are the image processing steps which are used in the proposed system. >Color Image to Gray Image Conversion Therefore, images are converted into gray scale images so that they can be handled easily and require less storage. The following equation shows how images are converted into gray scale images. I(x,y)=0.2989*B +0.5870*G +0.1140*B > Image Filtering The PSNR value is calculated for both the average and median resulting images .The average filter provides better result as compared to the median filter. So this paper uses average filter for further processing. > Image Segmentation To detect the pests from the images, the image background is calculated using morphological operators which is most critical after this image is subtracted from the original image. So the resulting image will only have the objects with pixel values 1 and background pixel values 0. >Noise Removal Noise contains dew drops, dust and other visible parts of leaves. As only the object of interest was to be visible on the images,so the aim was to remove the noise to get better and effective results. The Erosion algorithm has been used to remove isolated noisy pixels and to smoothen object boundaries . After noise removal,the next goal was to enhance the detected pests after segmentation which was performed by using the dilation algorithm. >Feature Extraction Different properties of the images are calculated on the basis of those attributes using which image is classified. For image properties, gray level co-occurrence matrix and regional properties of the images are calculated. These properties are used to train the support vector machine to classify images. >Counting of the pests on the leaves is the main purpose, so that it can give an idea of how much pests are there on a leaf.It uses Moore neighborhood tracing algorithm and Jacob's stopping criterion Feasibility: The present framework of pest detection is quite tedious and laborious for the farmers as they have to carry out their acre-acres surveys themselves and it requires a lot of vigorous efforts to achieve the same.Image analysis provides a realistic opportunity for the automation of insect pest detection.Through this system, crop technicians can easily count the pests from the collected specimens, and right pests’ management can be applied to increase both the quantity and quality of production. Using the automated system, crop technicians can make the monitoring process easier. So in order to bring enhancements in the system,we came up with more productive and well organised system with our idea .Due to this automaton applied,lucrativeness increases and labour is reduced.

Underwater images often have severe quality degradation and distortion due to light absorption and scattering in the water medium. A hazed image formation model is widely used to restore image quality. In this project, with the use of image processing algorithms, it is possible to reconstruct and enhance the image quality of underwater images in the absence of reliable and costly image acquisition systems. We propose an effective underwater image enhancement method for the underwater image in the composition of underwater image restoration and color correction. A fusion algorithm is proposed for the restoration and enhancement of underwater images. Color balance, contrast optimization, and histogram stretching are carried out. Results show that the quality of underwater images is improved significantly

9 Email Marketing Tips For Content Marketers Even “agnostics” regarding email marketing can't hash out the following evidence - the average ROI from this promotional practice is close to 3,800%. Measureless opportunities to scale up and relative cheapness, compared to other reaching-out channels, are the two reasons why the email marketing is fair-haired by businesses. However, this is not about the price and physical extent alone. The chief advantage is a better alignment of communication with customers. If you hope a certain content strategy brings desirable results, overlooking the quality of mailing messages will be a sorry pitfall. Always keep in mind that newsletters, welcome, retention, and other emails are not just a brand's facade - but a powerful tool for generating conversions. By joining sides of email and content strategies, you can come up with synergy from both. In this guide, we’ll cover a few recommendations for content marketers on how to write email messages that work. Tips for email marketing Segment your list Split the batch of email recipients into smaller groups based on chosen criteria, and mail distinct relevant messages - for each. You can use recipients' GEO, demographic characteristics, or purchase history to distinguish homogeneous clusters and proceed with the content planning. Segmentation is the basic premise for personalization, and if you still doubt why bothering about the latter - here are just a few numbers we took from Instapage: 52% of customers claim they do care if the message was tailor-made or not 82% of marketers say that mail personalization increases the open ratio custom emails have 41% more unique clicks than mass-produced ones. To avoid a fragmented approach, use data from CRMs, website analytics tools, and other sources to define segments. Concerning phrasings, a good idea is to create Buyer personas profiles. Thus, you'll be able to choose the appropriate message length and wording. Say, design a newsletter to promote paid subscription for an email validator service. You've decided to distinguish corporate clients based on their company size and determined the following groups: #1 - B2Bs and #2 - sole entrepreneurs. Possible messages for the two: #1. Our "XXL" plan is perfect for agencies and enterprises. One can add unlimited users and conduct up to 100,000 checks per month. #2. With our "S" you get 1,000 credits and 5,000 unique recipients - for only $33 per month. Plus - a 7-days free trial. Use interactive content The best content marketers know that interactive content came into vogue a long time ago. As to emails, here are the most common examples: CSS animated buttons If you include CTAs buttons (that we hope you do) - liven them up a bit. Add an animated hover effect, so that every time a recipient puts a cursor on a button, it changes shape, shade, color, or text. “Add hover to emphasise objects”, source This shouldn’t necessarily be something dramatic - add tiny accents that will yet grab the user's attention. starring “Add a star rating component to engage readers with content”, source Including ranking or reviewing widgets in the email body is one of the most working ways to engage the reader with the message. Ask recipients to assess your product or service with stars. Add the link to Google Forms if you want to receive an extended opinion on overall customer satisfaction. pictures' rollovers “Use animated images to describe goods better”, source The effect is eagerly used by the ones who promote online stores. Using The rollover allows to show goods from different angles or even play with recipients, if relevant. Take into account that this feature only works on desktops - mobile mail users will see the very first picture only. images carousel “Add pieces of text directly on images”, source If you want to enhance goods cards with descriptive content, say - price and shipping details, use a carousel instead of a rollover. As so, you can add more info pictures to the email body and, hopefully, convert more recipients into customers. a countdown “Countdowns work well for limited in time offers”, source Again, this type of interactive content fits the online shopping niche. Animated clocks amplify urgency and theoretically increase conversions. But it's important to stay extremely careful and not to sound desperate - otherwise, the newsletter will end up in the recipient's "Spam". Improve design The attractiveness of an email is something granted on certain terms, indeed. Not all emails need to be flashy or include expensive designs. However, there are some prevailing common trends in the matter. By following them, you seem to show the recipient that your company is moving in step with the times, and not stuck in the 2000s. Here's the shortlist from the TOP email design trends list that a 99designs provides - as of 2021: magazine-styled “Make newsletters to look a bit editorial”, source More and more newsletters tend to look like a centerfold from good old printed media. With a strict following to the "Less is more" principle - clear fonts, short phrases, HD-quality images with a few objects on them, and short CTAs. hand-made illustrations “Unique pictures create a distinct flavour of your brand”, source Tailored icons or sketchy images - whatever fits your mailing purpose, just make sure it's not too bright, contrast, or overloaded with details. Give preference to clean colors. skeuomorphic objects This is when a design resembles a real object. To see an example - just open a reader App on your smartphone. “A skeuomorphic bookshelf”, source HD photographies “If you operate in the luxury segment, do not skimp on email visuals”, source These are expensive content, but if you work in fashion or other chick industries - it may be worth the effort. animated content Yeap, we've covered this in a previous tip. single scroll “Looks especially good on smartphones”, source Place the entire email content, including buttons, on the endless-looking long frame. Focus on conversions Stay focused on what's your mailing purpose. Don't forget that everybody expects to see a good ROI from email actions at the end of the reporting period. Craft effective CTAs - perceive these not as a sole button with a "Download now" text or so, but as an entire sense of a message that you write. To create a captivating CTA copy, adhere to the below advices: include win-win propositions Even though you’re not providing a customer with a discount or cash refund at the moment, your proposition may include a non-monetary incentive. New arrivals, selection of the latest news, free copies, advice from experts - the only rule here is to offer what’ll hold in high esteem. trigger on emotions Don't long-windedly list benefits. Instead, simulate a life situation and show how your product or service can help. use several CTAs throughout the email Email body may be viewed in several scrolls, especially when via small mobile devices’ screens. If you add a call to action at the beginning of the message, a mere number of users will get back to it after finishing reading the content. Thus, you may lose potential conversion. Include several buttons throughout the email body, but don’t sound repeatedly - change calls’ forms and wording. Encourage readers to reply Driving recipients to reply is challenging yet able to be done. First, choose the proper writing tone. According to an extensive study of emails that didn’t get a response, the most preferable is a 3rd-grade reading level. “Too elementary or too proficient tone may scare away readers”, source Of course, you must apply this recommendation with an eye on the recipient. If you mail to a professor or a government agency, a “3rd-grade” rule isn’t applicable. But all else being equal - simplify the lexicon to the level a schoolchild can understand it. Another trick is to sound overall happy. Emails that are enhanced with positive emotions get 10-15% more replies, on average than neutral ones. The best manner is to choose a slightly warm tone. Exaggerated excitement may look weird and even suspicious, especially when reaching out to business partners. And don’t forget about courtesy. A rare person will respond if you address him or her with a hair-raising “To whom it may concern” phrase. Make it personal Personification shouldn’t be confused with personalization. The second is rather about mailing fitting content from a commercial perspective, while the first term - about addressing the recipient as a one-off personality. Personal emails start with the recipient’s name - and no other way. They include references to the user's interests or past actions. For example, if your tourist agency’s client is interested in island vacations - you shall approach him or her with respective offers. They also shall contain personalized promotions, if any. The best way to expand this approach on hundreds or thousands of recipients is to launch trigger-based email campaigns. Create delivery scenarios for different segments or stages of a sales pipeline. Then prepare a fitting sequence of relevant content - for every single scenario. To give a human face to mailing, one can practice greetings, as well. Birthdays, state holidays, anniversaries, a new status in the loyalty system - there are a lot of examples of what one may congratulate the customer with. Keep your emails out of spam folders It is better not to launch mailing at all than to use an untrustworthy emails’ database. The risks are much higher than a slew of undelivered messages - from harming a sender's reputation to being banned by mailing systems. So it's better to stay proactive: tidy away broken, misspelled, temporary, or other worrisome emails from the database - either manually or with the help of software collect a valid email address only - through email finders avoid spam-trigger words establish a double opt-in validation set the correct mailing frequency. Make sure your emails look clean and crisp Newsletters shall afterall bring revenues - whether you want it or not. But in a bid of quantity, don’t lose the overall content integrity and sense: a subject line, pre-header, header, email body, and calls shall be consistent with one another the copy must be of the proper size; although the length depends on many factors, stick to an “ideal” interval - 50 to 125 words if can, don’t attach too many files or links to external websites - mailing filters are suspicious to these adapt the layout to fit smaller screens - nothing looks worse than broken email elements when you open it on mobile. Wrapping up It doesn't make much difference whether you create mailing content for personal or business purposes - these email marketing tips will serve both. No strains here - the recipient’s interest should be at your forefront. If you can hook him or her with the content by using tricks we've covered, you’ll never fail with enough conversions.

Enhance your images with this Python toolkit! Includes color correction, histogram equalization, sharpening, old photo restoration, and upscaling (using interpolation) techniques for improved image quality. Perfect for photography, image processing, and computer vision projects.

A Python-based framework for converting 2D sketches and grayscale images into realistic 3D objects. Combines Pix2Pix, Real-ESRGAN, DeOldify, and ShapE models to enhance image quality, colorize, and generate 3D point clouds for detailed reconstructions.

EnhanceAI is an AI-powered image enhancement tool designed to improve image quality through denoising, super-resolution, and color correction techniques.

Colorizing black/white images and videos using a pre-trained model, aims to enhance visual quality of historical content

Find in Internet other greyscale and color images with low contrast. Try to improve their quality with histogram equalization. Use proper approach for color images.Calculate the histogram for each sub-region of the image (for example 4 rows and 3 columns).Use CLAHE with YUV color space to enhance color images.Is it possible to use HSV color space to increase contrast of color images?

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Enhance underwater images to improve visibility, color fidelity, and contrast using techniques like color correction, histogram equalization, unsharp masking, and image fusion. This project employs Python and libraries such as NumPy, leveraging advanced algorithms to restore image quality.

Enhancerkar is an AI-driven image enhancement tool that automatically improves image quality in seconds. With features like noise reduction, sharpness enhancement, and color adjustment, it provides users with a simple and fast way to optimize their images for personal, professional, or social media use.

A UNET based model for colourizing black and white images using Python. The model leverages convolutional neural networks to automatically add realistic color to grayscale images, enhancing visual quality and restoring historical photos.

Developed an AI-powered application that transforms images into cartoon-style visuals using Python and OpenCV. Implemented edge detection and color clustering algorithms to achieve a cartoon effect. Integrated image processing techniques for noise reduction and color enhancement, delivering high-quality, visually appealing results.

An intelligent image enhancement tool inspired by Renaissance techniques. Features automatic parameter detection, advanced color preservation, quality metrics, and parallel batch processing. Perfect for photographers and developers seeking to transform ordinary images with artistic precision.

This project, developed during my internship at NullClass EdTech, explores advanced image colorization using deep learning. It features a GAN-based model for grayscale images, a cross-domain model for RGB, sketches, and infrared inputs, and a user-friendly interface. Data augmentation techniques , demonstrating enhanced image quality.

Enhancing underwater image visibility using the Stable Diffusion model with the DreamBooth algorithm is an advanced approach to improve the quality of underwater photographs. Underwater images often suffer from issues like blurriness, color distortion, and low contrast due to light absorption and scattering.

Enhance the quality of low-light images with ease. This project utilizes CLAHE (Contrast Limited Adaptive Histogram Equalization) to improve visibility and reduce noise in images captured under challenging lighting conditions. Apply CLAHE to the Red, Green, and Blue channels of RGB images while preserving their original color.

Developed an AI-powered image processing tool that uses a Deep learning colorization model (Caffe) with 86% accuracy to restore colors in black-and-white images. Implemented a Wiener filter-based deblurring algorithm with Fourier transforms to enhance image clarity and used PyTesseract and OCR to extract text from image.

Fractal-Based Image Enhancement and Colorization Pipeline for Improved Visual Quality and Quantitative Evaluation

Image Enhancer Pro is an AI-powered tool that enhances image quality and resolution. It offers features like upscaling, noise reduction, sharpening, and color correction.

An intelligent AI-based image enhancement tool designed to improve image quality through advanced color optimization, detail sharpening, and natural visual enhancement

An AI-powered image enhancement system leveraging Deep Learning (TensorFlow/PyTorch) for super-resolution, denoising, and colorization to significantly improve image quality.

**Image Enhancement in Python**: Enhance visual quality using libraries like Pillow, adjust brightness, contrast, color, sharpen, remove noise, upscale resolution, and even apply machine learning for advanced enhancements. Python is a versatile tool for improving image quality.

A Visual Basic application that converts color images to grayscale using multiple algorithms to enhance image quality and create classic black-and-white effects.

A powerful C++ and OpenCV based photo enhancement tool that improves image quality using advanced techniques like sharpening, denoising, color correction, and super-resolution.

Explainable AI system that evaluates and enhances the visual quality of real estate images through lighting, sharpness, composition, color balance, and visual clutter analysis.

An image manipulation project implementing advanced techniques such as color correction, enhancement, object removal, and compositing. It combines traditional image processing and machine learning to improve image quality, usability, and automated visual editing.

This project implements underwater image processing methods for color correction and image enhancement. The project aims to restore realistic colors and enhance the visual quality of underwater images, compensating for the distortions caused by light absorption and scattering in underwater environments.

A collection of high-quality images of different iPhone models. Dynamic iPhone Image and Background Color Change: By clicking on any iPhone image at the bottom, the main displayed image and the background color of the page change to enhance visual interaction

The Underwater Image Enhancement project aims to improve the quality and clarity of underwater images, which are often affected by color distortion, low contrast, and blurriness due to water conditions.