Projects:2020s2-7233 Image Denoising with Dictionaries - Revision history

A1789660 at 12:59, 14 September 2020

2020-09-14T12:59:30Z

A1789660 at 12:26, 14 September 2020

2020-09-14T12:26:57Z

A1789660 at 12:04, 14 September 2020

2020-09-14T12:04:21Z

A1789660 at 12:01, 14 September 2020

2020-09-14T12:01:27Z

A1789660 at 11:59, 14 September 2020

2020-09-14T11:59:46Z

A1789660 at 11:55, 14 September 2020

2020-09-14T11:55:14Z

A1789660 at 11:47, 14 September 2020

2020-09-14T11:47:49Z

A1789660 at 11:46, 14 September 2020

2020-09-14T11:46:32Z

A1789660 at 11:45, 14 September 2020

2020-09-14T11:45:22Z

A1789660 at 11:43, 14 September 2020

2020-09-14T11:43:42Z

@@ Line 1: / Line 1: @@
 == Abstract ==
-Image denoising is essential phase in the pre-processing of images. Different algorithms have been proposed in the past few decades with varying denoising capabilities. K-Singular Value Decomposition algorithm is currently the most widely used adaptive learning dictionary in image denosing problem.
+Image denoising is essential phase in the pre-processing of images. Different algorithms have been proposed in the past few decades with varying denoising capabilities. K-Singular Value Decomposition (K-SVD) algorithm is currently the most widely used adaptive learning dictionary in image denosing problem. In this study, K-SVD algorithm will be implemented in image denoising and observe the performance when using different image datasets.
 == Introduction ==
 Dictionary learning is a topical area of signal processing research. It shares similarities but also crucial differences from other machine learning ideas such as deep learning neural networks. It rests on the idea of sparse representations using a well-designed dictionary that can lead to high performance in a wide range of signal processing applications. Image denoising is one of many signal processing applications. The idea developed here can easily be transferred to other application area.
@@ Line 13: / Line 13: @@
 * To analyse performance of dictionary learning on various image datasets
 * To implement K-SVD method image denoising
-* To test the effectiveness of different matching pursuit of algorithms in dictionary learning
+* To evaluate the effectiveness of different matching pursuit of algorithms in dictionary learning

@@ Line 1: / Line 1: @@
 == Abstract ==
+Image denoising is essential phase in the pre-processing of images. Different algorithms have been proposed in the past few decades with varying denoising capabilities. K-Singular Value Decomposition algorithm is currently the most widely used adaptive learning dictionary in image denosing problem.
 == Introduction ==
 Dictionary learning is a topical area of signal processing research. It shares similarities but also crucial differences from other machine learning ideas such as deep learning neural networks. It rests on the idea of sparse representations using a well-designed dictionary that can lead to high performance in a wide range of signal processing applications. Image denoising is one of many signal processing applications. The idea developed here can easily be transferred to other application area.

@@ Line 3: / Line 3: @@
 == Introduction ==
 Dictionary learning is a topical area of signal processing research. It shares similarities but also crucial differences from other machine learning ideas such as deep learning neural networks. It rests on the idea of sparse representations using a well-designed dictionary that can lead to high performance in a wide range of signal processing applications. Image denoising is one of many signal processing applications. The idea developed here can easily be transferred to other application area.
-== Project Team ==
+=== Project Team ===
-=== Project Students ===
+==== Project Students ====
 * Muhammad Haziq Saharuddin
 * Muhammad Faizal Azhar
 * Chen Chen
-=== Project Supervisor ===
+==== Project Supervisor ====
 * Associate Professor Brian Ng
 === Objectives ===

@@ Line 10: / Line 10: @@
 === Project Supervisor ===
 * Associate Professor Brian Ng
-== Objectives ==
+=== Objectives ===
 * To analyse performance of dictionary learning on various image datasets
 * To implement K-SVD method image denoising
 * To test the effectiveness of different matching pursuit of algorithms in dictionary learning

← Older revision		Revision as of 11:59, 14 September 2020
Line 11:		Line 11:
	* Associate Professor Brian Ng		* Associate Professor Brian Ng
	== Objectives ==		== Objectives ==
		+	* To analyse performance of dictionary learning on various image datasets
		+	* To implement K-SVD method image denoising
		+	* To test the effectiveness of different matching pursuit of algorithms in dictionary learning

← Older revision		Revision as of 11:45, 14 September 2020
Line 4:		Line 4:

	== Project Team ==		== Project Team ==
		+	=== Project Students ===
		+	=== Project Supervisor ===
		+	== Objectives ==

← Older revision		Revision as of 11:43, 14 September 2020
Line 1:		Line 1:
−	Abstract	+	== Abstract ==
		+	hello
		+	== Introduction ==

−	~~Introduction~~	+	== Project Team ==
−
−	Project Team