Multiresolution analysis

About: Multiresolution analysis is a research topic. Over the lifetime, 4032 publications have been published within this topic receiving 140743 citations. The topic is also known as: Multiresolution analysis, MRA.

An Efficient Pan Sharpening via Texture Based Dictionary Learning and Sparse Representation

Abstract: Remote sensing image pan sharpening has attracted researchers’ interest, since spatial resolution of multispectral (MS) image can be enhanced by injecting spatial details of a panchromatic image to MS image. In this paper, a novel sparse representation based pan sharpening method is proposed to overcome the disadvantages of traditional methods such as color distortion and blurring effect. This learning based method utilizes a compact single dictionary generated from texture information of high-resolution MS images in order to provide more effective and robust pan sharpening. Two data sets acquired from IKONOS and Quickbird satellites are used to evaluate the performance and robustness of the proposed algorithm. The proposed method is compared with nine well-known component substitution and multiresolution analysis methods and a state-of-art method using several quality measurement indices with references. The experimental results demonstrate that the proposed algorithm is competitive or superior to other conventional methods in terms of visual and quantitative analysis, as it preserves spectral information and provides high quality spatial details in the final product image.

Multiresolution 3D mesh compression

Abstract: In this paper, we propose an efficient low complexity compression scheme for densely sampled irregular 3D meshes. This scheme is based on 3D multiresolution analysis (3D discrete wavelet transform) and includes a model-based bit allocation process across the wavelet subbands. Coordinates of 3D wavelet coefficients are processed separately and statistically modeled by a generalized Gaussian distribution. This permits an efficient allocation even at a low bitrate and with a very low complexity. We introduce a predictive geometry coding of LF subbands and topology coding is made by using an original edge-based method. The main idea of our approach is the model-based bit allocation adapted to 3D wavelet coefficients and the use of EBCOT coder to efficiently encode the quantized coefficients. Experimental results show compression ratio improvement for similar reconstruction quality compared to the well-known PGC method.

Deep Adaptive Wavelet Network

Abstract: Even though convolutional neural networks have become the method of choice in many fields of computer vision, they still lack interpretability and are usually designed manually in a cumbersome trial-and-error process. This paper aims at overcoming those limitations by proposing a deep neural network, which is designed in a systematic fashion and is interpretable, by integrating multiresolution analysis at the core of the deep neural network design. By using the lifting scheme, it is possible to generate a wavelet representation and design a network capable of learning wavelet coefficients in an end-to-end form. Compared to state-of-the-art architectures, the proposed model requires less hyper-parameter tuning and achieves competitive accuracy in image classification tasks. The Code implemented for this research is available at https://github.com/mxbastidasr/DAWN_WACV2020