In this thesis, we propose a novel semi-supervised clean-noisy datasets adaptation algorithm. We transfer the knowledge learned on clean images to unlabeled noise-distorted ones. This modification on standard deep networks produce stable classification performance on all distortion levels, which brings benefit to real-world cases. Specifically, we propose a strategy to jointly learn a shared fe...

Abstract Remote sensing image edge detection is the basis of remote registration and recognition. In view noise formed in process acquisition, a new algorithm based on directional filter proposed to improve accuracy The wavelet transform denoising, three main features including scale, direction shape wave are introduced. target can be identified more accurately noisy by filter, result obtained ...

Segmentation of adjoining objects in a noisy image is a challenging task in image processing. Natural images often get corrupted by noise during acquisition and transmission. Segmentation of these noisy images does not provide desired results, hence de-noising is required. In this paper, we tried to address a very effective technique called adaptive wavelet thresholding for de-noising, followed...

Noisy channels have significant influence on the quality of image transmission as the error rates increase and a degrading affect at higher error rates. This paper presents a recovery scheme for image transmission over noisy channels based on a hybrid transform and error correcting codes with error replacement strategy. In this paper we describe allocating redundant bits based on some importanc...