In image denoising networks, feature scaling is widely used to enlarge the receptive field size and reduce computational costs. This practice, however, also leads loss of high-frequency information fails consider within-scale characteristics. Recently, dynamic convolution has exhibited powerful capabilities in processing (e.g., edges, corners, textures), but previous works lack sufficient spati...

in many autonomous mobile applications, robots must be capable of analyzing motion of moving objects in their environment. duringmovement of robot the quality of images is affected by quakes of camera which cause high errors in image processing outputs. in thispaper, we propose a novel method to effectively overcome this problem using neural networks and kalman filtering theory. thistechnique u...

Denoising or usually known as noise reduction is one of the most essential processes for digital image processing. The goal of denoising is how to remove the noise while keeping the important image features as much as possible. In this paper, a denoising algorithm using adaptive block-based singular value decomposition filtering is presented. The proposed approach, instead of applying block-bas...