Seamless Heterogeneous 3D Tessellation via DWT Domain Smoothing and Mosaicking

Hayat, Khizar; Puech, William; Islam, Naveed; Gesquière, Gilles

doi:10.1155/2010/913681

Cited by 3 publications

(3 citation statements)

References 14 publications

(18 reference statements)

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“…But what the author suggests is on the contrary. If the aim is just to reduce the size, why not partition the HR image to small tiles, feed it in lieu of low resolution image to the pipeline to get the super-resolved tile and then stitch or tessellate [119] the output tiles? The second issue is the claim of best real-time performance on the basis of a few datasets.…”

Section: B State Of the Art Methods On Image Srmentioning

confidence: 99%

Multimedia super-resolution via deep learning: A survey

Hayat

2018

Digital Signal Processing

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The recent phenomenal interest in convolutional neural networks (CNNs) must have made it inevitable for the super-resolution (SR) community to explore its potential. The response has been immense and in the last three years, since the advent of the pioneering work, there appeared too many works not to warrant a comprehensive survey. This paper surveys the SR literature in the context of deep learning. We focus on the three important aspects of multimedia -namely image, video and multi-dimensions, especially depth maps. In each case, first relevant benchmarks are introduced in the form of datasets and state of the art SR methods, excluding deep learning. Next is a detailed analysis of the individual works, each including a short description of the method and a critique of the results with special reference to the benchmarking done. This is followed by minimum overall benchmarking in the form of comparison on some common dataset, while relying on the results reported in various works. Fig. 1: Backpropagation (after [7]).acceptable level of convergence whereby the optimized parameters should ideally classify each subsequent test case correctly. The birth of Convolutional neural networks (CNN) or ConvNets can be traced back to 1988 [15] 1 wherein backpropagation was employed to train a NN to classify handwritten digits. Subsequent works by LeCun evolved into what was later known as LeNet5 [17]. After that there's virtual lull till late noughties [18] when GPUs were efficient enough to culminate in the work [19]. Since then a floodgate has opened and we hear of various architectures in the form of AlexNet [20], ZFNet [21], GoogLeNet [22] DenseNet [23] etc.; for a detailed overview one can consult [18], [24].The metamorphosis from fully connected NN to locally connected NN to CNN is illustrated in Fig. 2. As can be seen, rather than being fully connected, the CNN employs convolutions leading to local connections, where each local region of the input is connected to a neuron in the output. The input to a CNN is in the form of multiple arrays, such as a color image with three 2D arrays (length × width) in accordance to RGB or YCbCr channels. The number of channels is called depth and constitutes the 3rd D; note that more than three channels are not uncommon, e.g with hyperspectral images. A CNN is made up of Layers with each layer transforming an input 3D volume to an output 3D volume [11], typically, via four distinct operations [25], viz. convolution, a non-linear activation function (ReLU), pooling or sub-sampling and classification (fully connected Layer). A simplified CNN is illustrated in Fig. 3 2 . A CNN can be described as several convolution layers with nonlinear activation functions (e.g. ReLU or sigmoid) applied to each layer. Each convolution layer applies several (may be thousands) distinct filters 3 (also called feature maps) and combines their results. These filters are automatically learnt during the training part based on the task in hand, e.g. if the task is image classification the learning concerns, a...

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Section: B State Of the Art Methods On Image Srmentioning

confidence: 99%

Multimedia super-resolution via deep learning: A survey

Hayat

2018

Digital Signal Processing

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“…In DWT, Hayat et al [ 19 ] presented two transform domain methods to seamlessly stitch satellite image tiles of heterogeneous resolutions. One is local, and each constituent DWT domain tile of the view is treated at sub-band level with horizontal, vertical, and radial smoothing on the basis of its locale in the tessellation.…”

Section: Literature Reviewmentioning

confidence: 99%

Seamless Copy–Move Replication in Digital Images

et al. 2022

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The importance and relevance of digital-image forensics has attracted researchers to establish different techniques for creating and detecting forgeries. The core category in passive image forgery is copy–move image forgery that affects the originality of image by applying a different transformation. In this paper, a frequency-domain image-manipulation method is presented. The method exploits the localized nature of discrete wavelet transform (DWT) to attain the region of the host image to be manipulated. Both patch and host image are subjected to DWT at the same level l to obtain 3l+1 sub-bands, and each sub-band of the patch is pasted to the identified region in the corresponding sub-band of the host image. Resulting manipulated host sub-bands are then subjected to inverse DWT to obtain the final manipulated host image. The proposed method shows good resistance against detection by two frequency-domain forgery detection methods from the literature. The purpose of this research work is to create a forgery and highlight the need to produce forgery detection methods that are robust against malicious copy–move forgery.

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“…The main problem of this scheme is that the number of JTiles generated at every resolution level is always the same, and its size is decreased ( Figure 1 -b), so this scheme do not match with the tiles used in a tiled pyramid (where the number of tiles decrease and its size remains constant) and it will be necessary to transmit and combine several of these JTiles to obtain an equivalent tile of the tiled pyramid. These differences can be observed comparing Figure This strategy is used by Hayat et al (2010) to do online 3D terrain visualization.…”

Section: Strategy 2: Jpeg2000 With Tilingmentioning

confidence: 99%

A New Terrain Data Compression Scheme for Interactive Terrain Visualization Systems

Olanda¹,

Pérez²,

Orduña³

2011

Proceedings of the International Conference on Computer Graphics Theory and Applications

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Over the last years, there has been a great development on real time terrain visualization applications using remote databases. One of the main problems that these applications must face is the storage and transmission of terrain data. Despite the considerable bandwidth increase of internet connection during last years, the large amount of data to be transmitted can easily saturate these connections. On other hand, since data must be stored in the client side, clients need a considerable storage space. In this context, we propose a new compression scheme that solves or minimizes these problems. It is based on JPEG2000 standard; however, the wavelet analysis and synthesis algorithms are modified to allow efficient transmission and reconstruction of terrain data by using tiled pyramids multiresolution techniques. A comparative study including current techniques shows that the proposed scheme obtains a better compression ratio of the terrain data, reducing the storage space and transmission bandwidth needed, achieving a better visual quality of terrain data reconstructed after data decompression.

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Seamless Heterogeneous 3D Tessellation via DWT Domain Smoothing and Mosaicking

Cited by 3 publications

References 14 publications

Multimedia super-resolution via deep learning: A survey

Multimedia super-resolution via deep learning: A survey

Seamless Copy–Move Replication in Digital Images

A New Terrain Data Compression Scheme for Interactive Terrain Visualization Systems

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