Image analysis is the extraction of meaningful information from images. Detecting the geometric content of images is a useful tool for shape analysis and object detection and recognition. In Saiwa, we propose two services. First, Line Segment Detector (LSD) to detect line segment primitives. Second, Arc Line Detector (ALD) to jointly detect line segment, elliptical arc and circular arc primitives.
Contrast enhancement improves the perceptibility of objects in a scene by increasing the difference in brightness between objects and their backgrounds. Contrast enhancement of images has a wide range of applications such as medical imaging, industrial machine vision, and astronomical imaging. With the Saiwa Contrast Enhancement Demo, you can test a fast and robust local enhancement method called LLCC on your own images.
Image restoration is the process of recovering an image from a degraded version. Image restoration is a critical step in many image processing applications such as medical imaging, remote sensing, security, and digital forensics. Saiwa offers three important restoration services: Denoising, Deblurring, and Deraining.
Resolution enhancement, or super-resolution, is the process of increasing the resolution of an image or video by generating missing high-frequency detail from low-resolution input. The goal is to produce an output image with a higher resolution than the input image while preserving the original content and structure. Image enhancement has many applications, including security and surveillance imaging, medical imaging, image generation, and satellite and astronomical imaging. In Saiwa’s resolution enhancement service, two CNN-based methods are provided: Residual Dense Network (RDN) and Residual in Residual Dense Network (RRDN).
Image inpainting is a technique that can reconstruct missing or damaged regions in an image using information from surrounding pixels. It can be used for various image processing tasks, such as removing unwanted objects, enhancing old photos, filling gaps, or creating artistic effects. Saiwa proposes a two-step generative method for deep image inpainting called DeepFill v2. This method fills large and multiple areas of an image without the usual boundary artifacts, distorted structures, and blurred textures inconsistent with the surrounding areas that we observe in other deep networks.