deftruth/image-processing-algorithm-speed

This repository is a collection of heavily optimized image processing algorithms. It shows how to take common image editing tasks, like converting colors, detecting edges, applying filters, or resizing, and make them run dramatically faster on standard computer processors. The project works by using special hardware features called SSE and AVX2 instruction sets. Think of these as shortcuts built into computer chips that let the processor handle multiple pieces of data at the same time instead of one at a time. The author also uses multi-threading (splitting work across multiple cores) and clever mathematical tricks to cut processing time. For example, converting a 12-megapixel photo from RGB to grayscale takes about 12 milliseconds with a basic approach, but the final optimized version brings that down to under 3 milliseconds. A median filter that originally took over 8 seconds drops to under 10 milliseconds. The main audience is C++ developers working on performance-critical image processing, particularly in applications like real-time camera systems, photo editing software, or computer vision pipelines where every millisecond matters. Someone building a smartphone camera app or an industrial inspection system could study these implementations to understand how to make their own image filters run fast enough for live video. The benchmark tables make it easy to see exactly how much each optimization step contributes. A notable aspect of the project is its transparency about tradeoffs. Not every optimization works, the author openly reports that SSE provided no speedup for integral image computation. The bicubic zoom optimization, while much faster than a basic implementation, still loses to OpenCV's built-in function. This honesty makes it a practical learning resource: you see what works, what doesn't, and why. The code is built on OpenCV and targets Intel processors, with testing done on CPUs ranging from an older i5-3230 to an i7-6700.